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| S-skattare för robust regression× | MM-estimering för robust regression× | Vanligaste minsta kvadratmetoden (OLS) Regression× | |
|---|---|---|---|
| Ämnesområde≠ | Statistik | Statistik | Ekonometri |
| Familj | Regression model | Regression model | Regression model |
| Ursprungsår≠ | 1984 | 1987 | 2019 |
| Upphovsperson≠ | Rousseeuw & Yohai (1984) | Victor J. Yohai | Wooldridge (textbook treatment); classical least squares |
| Typ≠ | Robust linear regression | Robust linear regression | Linear regression |
| Ursprungskälla≠ | Rousseeuw, P. J. & Yohai, V. J. (1984). Robust Regression by Means of S-Estimators. In Robust and Nonlinear Time Series Analysis (Lecture Notes in Statistics, Vol. 26, pp. 256-272). Springer. DOI ↗ | Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Alias≠ | S-estimation, robust S-regression, S-Tahmin Edici | MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Närliggande | 5 | 5 | 5 |
| Sammanfattning≠ | The S-estimator is a robust linear-regression method, introduced by Rousseeuw and Yohai in 1984, that estimates the coefficients by minimising a robust M-estimate of the residual scale rather than the variance of the residuals. By driving down a bounded measure of residual spread it can attain a breakdown point of up to 50%, so it stays reliable even when a large share of the data are outliers, and it provides the first stage of the well-known MM-estimator. | The MM-estimator is a robust linear regression method introduced by Victor J. Yohai in 1987. It combines the high breakdown point of an S-estimator with the high efficiency of an M-estimator, so it resists outliers strongly while still using the data efficiently when errors are well-behaved. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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